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COURSE DESCRIPTION
Industrial & Manufacturing
Engineering - Knowledge-Based Systems 3(3,0)
[IME 427 - Elective Course]
Spring Semesters 2007 – 2008
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2007-2008
Catalog Data:
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427-3 KNOWLEDGE-BASED SYSTEMS.
(Same as CE 427, ECE 427 and ME 427.)
Engineering-oriented perspective on artificial intelligence (AI)
technology. General AI concepts and specifically knowledge-based (expert)
systems applied to engineering problem-solving. Prerequisites: CS 145,
senior standing or consent of instructor.
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Textbook(s):
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Giarratano, Joseph and Riley, Gary,
Expert Systems: Principles and Programming, Third Edition, PWS
Publishing Company, Boston,
MA 1998
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Coordinator:
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Emmanuel S. Eneyo, Professor of Industrial and Manufacturing
Engineering.
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1. Introduction and overview (4.5 hours)
2. Knowledge acquisition (3 hours)
3. Knowledge representation (6 hours)
4. Inference techniques (3 hours)
5. Inexact reasoning (3 hours)
6. Knowledge-based design issues (4.5 hours)
7. Expert system development shells (6 hours)
8. Development of KBES application prototypes (12 hours)
9. Examinations excluding term project presentation (3 hours)
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Topics and
Schedule:
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This course introduces
design methodologies and user-centered design to senior-level and
graduate engineering students. The course is an engineering topics course
with significant engineering content through laboratory projects
including the following:
1. Students will be
required to carry out a minimum of three progressively challenging
assignments that parallel the lectures, using VP-EXPERT and/or KAPPA PC
or LEVEL 5 OBJECT as the "expert system development shell" with
its accompanied manual as an additional text for the course.
2. In addition to the
assignments given during the first half of the course, each student will
be expected to select a particular problem domain of interest that is
representative of the identified classes of engineering tasks suitable
for AI application. The students will be required to spend approximately
the last half of the course developing a knowledge-based (expert) system
for the selected problem as their term projects. With this exercise,
students will gain hands-on experience at encoding knowledge in different
representation schemes, particularly production rules.
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Course Outcomes
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Students successfully completing this course will possess:
1. An understanding of
knowledge acquisition techniques.
2. An understanding of
knowledge representation techniques.
3. The ability to perform
inference techniques such as:
i) Forward chaining
ii) Backward chaining
4. The ability to develop
prototype knowledge-based expert systems (KBES) for specific problem
domain.
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Prepared by:
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Emmanuel S. Eneyo, Professor of Industrial and Manufacturing
Engineering.
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Date:
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May 12, 2008
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Program Educational Objective.Outcome
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General Course
Outcomes
IME 427 –
Knowledge-Based Systems
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1
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2
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3
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4
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1.1
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1.2
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1.3
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1.4
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2.1
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P
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2.2
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P
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P
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2.3
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P
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2.4
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P
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P
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3.1
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P
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3.2
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P
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3.3
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P
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3.4
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3.5
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P
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4.1
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4.2
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4.3
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4.4
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